Various types of devices, sensors and techniques exist for determining implicit and explicit characteristics of people and places. Some systems use devices associated with a particular user to sense or determine user specific information. Sensors in or coupled to a mobile electronic device can sense various implicit indicators of characteristics for a particular user. For example, sensors in a smartphone can sense the physical properties, e.g., position, temperature, rate of motion, heartbeat, etc., of a particular user of the device to gather information that can imply characteristics for that particular user. Other conventional mobile electronic device based systems also gather information about particular users by providing mechanisms through which a user can explicitly report user characteristics, e.g., age, mood, state of health, weight, etc. For example, a smartphone can execute an application that prompts a user to explicitly enter personal information. These types of mobile implicit and explicit user characteristic collection devices only gather information for one user at a time. Typically, each mobile device only gathers information about the owner or the current user of the device.
Other systems use stationary sensors, such as cameras, infrared imagers, microphones, voice recognition, etc., to detect the characteristics of multiple people in a particular area in proximity to the sensors. Such systems can analyze the physical properties of the people to determine characteristics, e.g., mood, health, or demographic information, for the people in that particular location. For example, systems exist that can determine indications of health, e.g., fever, flu, cold, etc., of some portion of the people in a location based on the physical properties, such as the surface temperature of a person's face, for people who come within range of a particular sensor. Because the sensors in such systems are stationary, the results are limited to locations in which the sensors are installed. Furthermore, the resulting sample of a particular group or population within range of the sensors is limited. The limited sampling of the group of people can skew the results when interpolating, or otherwise determining, the mood or other characteristics associated with a given location.
FIG. 1 illustrates a diagram of a particular region 100. The region 100 can include a number of locations 120 in which various numbers of people 110 can be found. Some of the locations 120 can include a stationary sensor (SS) 115. As shown, the distribution of the stationary sensors 115 is limited to only a few of the possible locations 120. Accordingly, only locations 120 that include a stationary sensor 115 are capable of determining even an approximation of a characteristic, such as the mood, of some group of people 110 in a particular location 120 or region 100. In the specific example shown, only locations 120-1, 120-4, 120-6, 120-10, and 120-12 include stationary emotion sensors 115. The other locations 120 have no means for reliably determining the characteristics for those locations.
Furthermore, even locations 120 that are equipped with a stationary sensor 115 are limited by the ability of the sensor to detect only a limited sample of the people 110 in the location. The limits of the stationary sensors 120 can be based on the limits of the sensor in terms of range, speed, and accuracy. In addition, some people may actively avoid the stationary sensors 120. For instance, a mood detecting camera can be positioned at the front door of a given entertainment venue to capture the facial expressions of people as they enter the venue, and another mood detecting camera can be positioned near the performance stage of the same venue to capture facial expressions of people as they watch a performance. The facial expressions captured by the mood detecting camera at the front door of the venue might detect that a majority of the people entering the venue are excited, and the facial expressions captured by the mood detecting camera at the stage might detect that the majority of people near the stage are happy. However, there may be other people, or even a majority of people, in the venue not being imaged by either of the mood detecting cameras, who may be bored, tired, or unhappy with the entertainment or the venue. In such situations, any interpolated result or conclusion as to the overall mood of the people in the venue can be spurious, and thus, not represent the true mood or success of the venue in entertaining its patrons. Embodiments of the present disclosure address these and other issues.